The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for applying matching data transformation information based on a user's editing of data within a document.
Traditional extract, transform, and load (ETL) tools are flow oriented, such that developers first create data flow jobs based on business logic, run those jobs to generated processed data, and then write the processed data to a target. Cloud data preparation services are data oriented, where data engineers first load data to a shaper, directly apply transformation operations to the data, and then save the shaped data to a target. Although the concept around data integration is different, the challenge on how to improve developer/engineer productivity is similar. With traditional ETL tools, developers are facing the challenge on how to come up with an optimal design, where in cloud services, engineers are facing the challenge on how to navigate through hundreds of available operations to effectively find a correct operation needed for data preparation requirements. For instance, if a data engineer has to pick an operation from a long list of 500 items, no matter how the list is organized, it is always difficult and time consuming for the data engineer to find the right operation.